{"id":"W4360879252","doi":"10.2196/46348","title":"Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study","year":2023,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Radiology practices and education","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flagging; Computer science; Artificial intelligence; Negation; F1 score; Natural language processing; Encoder; Transformer; Security token; Speculation; Machine learning; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003061485,0.001313579,0.0006202913,0.001470441,0.0004172211,0.0008367853,0.001652254,0.001153445,0.002107857],"category_scores_gemma":[0.005962776,0.0003558847,0.0009355226,0.0007949395,0.0003847402,0.001737436,0.001355487,0.001362983,0.001072271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009502491,"about_ca_system_score_gemma":0.001379188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006177154,"about_ca_topic_score_gemma":0.007752782,"domain_scores_codex":[0.9983205,0.0004346762,0.0001964866,0.000465154,0.0004326711,0.0001504649],"domain_scores_gemma":[0.9961929,0.001680123,0.0002439119,0.000543296,0.001209242,0.0001304459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001420009,0.0007871105,0.01872651,0.0005739489,0.0003773482,0.0006641043,0.0003211958,0.03631674,0.03675932,0.0010425,0.01133307,0.8916781],"study_design_scores_gemma":[0.0002291237,0.001040495,0.01091305,0.0001345741,0.0003648117,0.001073167,0.0004052315,0.9022322,0.07391067,0.0017753,0.007851211,0.00007009396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6283838,0.005644281,0.3362633,0.0008252692,0.0004166462,0.0007891697,0.002972502,0.01893212,0.00577281],"genre_scores_gemma":[0.7554939,0.00114178,0.2317086,0.000442184,0.00006974898,0.0003182622,0.005856299,0.0003093002,0.004659892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006177154,"threshold_uncertainty_score":0.01619089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04012883448843974,"score_gpt":0.400929795150139,"score_spread":0.3608009606616993,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}